Start by clarifying the problem scope—what counts as harmful, what data is available, and what latency/scale requirements exist. Then propose an end-to-end ML system covering data collection, model training, serving, and monitoring, while discussing trade-offs between accuracy, latency, and privacy. Emphasize Apple's values around user privacy and on-device processing.
Pro tip: Show awareness of Apple's privacy-first approach by suggesting on-device inference or federated learning where possible, and discuss how you'd handle false positives/negatives given the sensitivity of harmful content.
Ask questions to understand what 'harmful' means (e.g., hate speech, harassment, misinformation), the scale (millions of texts per day?), latency needs (real-time vs batch), and privacy constraints. This ensures you design the right system.
Discuss sourcing labeled data: user reports, human review, synthetic data, and public datasets. Address challenges like label noise, bias, and privacy-preserving data collection (e.g., differential privacy).
Choose models (e.g., fine-tuned transformers like BERT, or smaller on-device models) based on accuracy vs latency trade-offs. Cover training pipeline, handling class imbalance, and evaluation metrics (precision/recall, F1, AUC).
Design serving architecture: on-device for privacy and low latency, or server-side for complex models. Discuss batching, caching, fallback mechanisms, and A/B testing for model updates.
Set up monitoring for model drift, false positive/negative rates, and user feedback loops. Plan for continuous retraining and human-in-the-loop review for edge cases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.